With the widespread popularity of electronic devices, especially the widely used mobile devices, users often use electronic devices to perform a variety of tasks, such as communications, web browsing, and micro-blogging, etc. Users often need to use some input methods to enter texts, including English words.
When entering an English word, a generic input method often uses an autocomplete or word association algorithm to make English word suggestions, such that, when a user does not remember the exact spelling of an English word, the user may enter a word prefix and obtain the correct word.
For example, when a user is trying to enter the word “successful”, the user may enter the first five letters “succe”, and an input method automatically searches words with a length more than 5 letters in a dictionary. If any word has the first 5 letters matching “succe”, this word is appearing as the candidate for word suggestions, such as “success”, “succeed”, successful”, etc. Thus, the user enters a partial spelling of a word, looks through automatically generated word suggestions, and selects a word the user intends to enter.
However, in the existing input methods, the letters entered by the user and the leading letters of words in the dictionary must match exactly to provide any associated words. When the user enters an incorrect partial spelling of an intended word, conventional input methods are unable to provide properly associated words. For example, when the user enters “scce”, conventional input methods are unable to provide properly associated words such as “success”, “succeed”, succession”, and “successive”, etc.
Thus, when a user entered partial spelling contains error, conventional input methods are unable to provide properly associated words. As a result, the user can incur high operation and time cost, and the input efficiency is low. In addition, the electronic device may have to respond repeatedly to page-turning operations, etc., wasting a lot of system resources.